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About This Role
COMPANY DESCRIPTION
At AirLife, we are dedicated to improving the quality of every breath. Excellence with Every Breath is not just a tag line, but the way we work and take care of our customers. With a mindset to evolve, innovate, and grow, we are a premier manufacturer of the highest-quality and market-leading breathing consumables. This growth philosophy has positioned us to increase our global footprint and business reach, impacting even more people around the world. Our expanding family of the most trusted brands offers a product portfolio that spans the continuum of care from first responder to home care, with safety, patient comfort, and clinical performance in mind. Collective expertise allows us to provide quality products and experiences to our patients, customers, and our people. Our values of Customer first, Differentiate with our People, Bias for Action, Continuous Improvement and Accountability define who we are and how we work. Join us!
POSITION SUMMARY
The Industrial Engineer will provide engineering and project support to Distribution Centers by combining traditional industrial engineering methods with applied AI and advanced analytics. Drive labor optimization by improving productivity through engineered labor standards, AI-driven forecasting and predictive models, KPI’s, automation technology, and continuous improvement initiatives supports day-to-day distribution center performance through data analytics, machine learning models, labor management analysis, and visual management tools. This role focuses on using statistical analysis, AI/ML techniques, and operational data to improve labor efficiency, productivity, predictive maintenance, and workflow visibility within the distribution center while supporting continuous improvement initiatives.
POSITION QUALIFICATIONS
Knowledge, Skills, & Abilities:
- Distribution center/logistics concepts – understanding of warehousing best practices.
- Ability to drive decision making through data and relevant business metrics.
- Ability to resolve problems in a timely manner with little or no supervision.
- Excellent verbal and written communication skills.
- Strong interpersonal skills and ability to work cross functionally.
- Must be able to utilize problem solving techniques to identify root causes of failures and solutions to meet targets.
- Strong business planning and analytical skills including market intelligence, metrics, financial and analysis.
- Ability to manage project portfolio of varying priorities and impact value.
- Strong computer skills including Power B.I, MS Office and MSD/MOST experience preferred; proficiency with Python, SQL, or similar tools for data analysis and predictive modeling a plus.
- Experience applying machine learning or statistical modeling to operational problems (e.g., demand forecasting, labor forecasting, predictive maintenance) preferred.
- Familiarity with simulation or discrete-event modeling tools (e.g., Arena, FlexSim, AnyLogic) a plus.
- Experience in developing engineered labor standards.
- Continuous improvement experience and knowledge of CI methodologies preferred.
- Experience within the distribution/warehouse/logistics industry preferred.
- Proficient in RF Scanning and Warehouse Management Systems preferred.
- Experience in semi and fully automated material handling systems a plus.
Level of Experience:
- 3+ years of work experience in Industrial Engineering.
- 1+years of work experience with AI application in supply chain or similar
Level of Education:
- Bachelor's degree or higher in Industrial Engineering or equivalent degree such as Supply Chain, Operations, Engineering, Logistics is required.
Travel:
- Up to 10%.
ESSENTIAL DUTIES AND RESPONSIBILITIES
- Develop, validate, and maintain time-studied standards for overall operation and for each function, leveraging AI-assisted time-and-motion analysis and predictive labor models where applicable
- Create a culture of performance management through leader standard work, productivity meetings and KPI
- Provide training to operations teams on labor management and associated systems
- Create a playbook of productivity improvements using CI/Lean principles and technology
- Develop and deploy AI/ML-based models (e.g., demand and labor forecasting, predictive maintenance) to support proactive, data-driven decision-making
- Build and maintain automated, AI-enabled dashboards and reporting tools to increase real-time visibility into labor, throughput, and equipment KPIs
- Write all SOP’s for operations and ensure employee training program in place
- Evaluate and make recommendations on layouts, MHE and other equipment to optimize productivity, using simulation and AI-driven scenario modeling to validate changes before implementation.
OTHER RESPONSIBILITIES
- Uphold and embody AirLife’s values in all aspects of work.
- Demonstrate accuracy and thoroughness in daily work; look for ways to improve and promote quality & safety.
- Inspire the trust of others; treat people with respect and dignity and embrace the value of diversity.
- Use time efficiently; perform job accurately, thoroughly, and conserve Company resources to improve profits.
- Contribute to building and maintaining a positive team environment.
- Assure all policies and guidelines are implemented and followed.
QUALITY POLICY
At AirLife, Quality is our promise. It is our commitment to customer satisfaction and our dedication to product excellence in an evolving global healthcare market. This promise is kept through a continuously improving and effective Quality Management System and compliance to Regulatory Requirements.
DEIA STATEMENT
At AirLife, we are committed to building a diverse workforce and an inclusive workplace that reflects the communities and customers we serve. We believe our philosophy on Diversity, Equity, Inclusion, and Advancement (DEIA) encourages excellence and equips us to serve an evolving global marketplace.
BENEFITS
AirLife offers a comprehensive benefits package, including medical, dental, and vision coverage, 401(k), paid time off, paid holidays, bereavement leave, Employee Assistance Program resources, and medical leave benefits, subject to applicable eligibility requirements. Certain positions may also be eligible for bonus, commission, or other incentive compensation.
Please note: The responsibilities outlined above are not exhaustive and may evolve over time. The role holder may be required to undertake additional duties as reasonably expected to meet the needs of the company.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 10,565 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At AIRLIFE, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,835 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,300. Top-quartile compensation starts at $264,195. The 90th percentile reaches $320,000. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,300; VP: $235,000.
AIRLIFE AI Hiring
AIRLIFE has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Glendale, AZ, US.
Location Context
Across all AI roles, 6% (610 positions) offer remote work, while 9,876 require on-site attendance. Top AI hiring metros: New York (2,874 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,339 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 10,565 open positions tracked in our dataset. By seniority: 217 entry-level, 5,017 mid-level, 3,793 senior, and 1,538 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (610 positions). The remaining 9,876 roles require on-site or hybrid attendance.
The market median for AI roles is $213,300. Top-quartile compensation starts at $264,195. The 90th percentile reaches $320,000. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 10,565 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (7,941), AI Software Engineer (498), Data Scientist (479). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (217) are outnumbered by mid-level (5,017) and senior (3,793) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,538 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (610 positions), with 9,876 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,300. Top-quartile roles start at $264,195, and the 90th percentile reaches $320,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,826 postings), Aws (1,033 postings), Azure (815 postings), Rag (713 postings), Gcp (581 postings), Pytorch (568 postings), Kubernetes (446 postings), Claude (413 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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